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Application of Artificial Neural Network in Cellular Handoff Management

Application of Artificial Neural Network in Cellular Handoff...

引用

International Conference on Computational Intelligence and Multimedia Applications

作者： P. P. Bhattacharya Dr. B. C. Roy Engineering College Durgapur India

An Artificial neural network based handoff algorithm is proposed and its performance is studied in this paper. Handoff decisions are normally signal strength based and recently many artificial intelligent techniques are used such as Fuzzy Logic, Artificial Neural Network (ANN) etc. In this paper, ANN is used for taking fast and accurate handoff decision. The algorithm is proposed, tested and found to give excellent results.

关键词： Artificial neural networks Cellular networks Base stations Neural networks Hysteresis Telecommunication traffic backpropagation algorithms Iterative algorithms Computational intelligence Computer network management

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Dynamically pruning output weights in an expanding multilayer perceptron neural network

Dynamically pruning output weights in an expanding multilaye...

引用

International Conference on Digital Signal Processing (DSP)

作者： H. Amin K.M. Curtis B.R. Hayes Gill Parallel Processing Specialist Group Department of Electrical & Electronic Engineering University of Nottingham Nottingham UK University of Nottingham Nottingham Nottinghamshire GB

The network size for a multilayer perceptron neural network is often chosen arbitrarily for different applications, and the optimum size of the network is determined by a long process of trial and error. This paper presents a backpropagation algorithm. For a multilayer perceptron (MLP) neural network, that dynamically determines the optimum number of hidden nodes and applies a new pruning technique on output weights. A 29% reduction in the total number of output weights was observed for a handwritten character recognition problem using the new pruning algorithm.

关键词： Multi-layer neural network Intelligent networks Neural networks backpropagation algorithms Multilayer perceptrons Neurons Character recognition Parallel processing Monitoring Mean square error methods

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Learning optimal linear filters for early vision

Learning optimal linear filters for early vision

引用

International Joint Conference on Neural Networks (IJCNN)

作者： X. Sun D.G. Lowe Department of Computer Science University of British Columbia Vancouver BC Canada

The authors describe experiments in which the rescaling back-propagation learning algorithm was used to learn sets of linear filters for the task of determining the orientation and location of edges to subpixel accuracy. A model of edge formation was used to generate input-output pairs for each iteration of the training process. The desired output included determining the interpolated location and orientation of the edge.< >

关键词： Nonlinear filters Image edge detection Detectors Computer vision Gabor filters Frequency Neurons backpropagation algorithms Sun Computer science

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Trajectory tracking using neural networks

Trajectory tracking using neural networks

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： H.M. Tai Department of Electrical Engineering University of Tulsa Tulsa OK USA

Presents a method for tracking prespecified trajectories in industrial drive systems with a multilayered feedforward neural network. The method utilizes the backpropagation technique to learn feedback-error ranges, in which appropriate control actions can be generated from a lookup table. It can follow arbitrarily prescribed trajectories even when they are not present in the training phase. This approach is simple and practical for real-time implementation. Examples are included to demonstrate the effectiveness. The analogy between this scheme and a fuzzy logic control strategy is also investigated.< >

关键词： Trajectory Neural networks Control systems Electrical equipment industry backpropagation algorithms Error correction Fuzzy logic Industrial control Adaptive control Signal generators

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Adaptation of neural network and application of digital ultrasonic image processing for the pattern recognition of defects in semiconductor

Adaptation of neural network and application of digital ultr...

引用

International Symposium on Electronic Materials and Packaging

作者： Jae-Yeol Kim Hyun-Jo Jeong Hun-Cho Kim Chang-Hyun Kim The Division of Mechanical Engineering Chosun University Kwangju South Korea The Division of Mechanical Engineering Wonkwang University South Korea The Korea Chamber of Commerce and Industry Kwangju South Korea The depature of Precission Mechanical Engineering Chosun Graduate School South Korea

ISBN: (纸本)0780371577

In this study, the classification of artificial defects in semiconductor devices are performed by using pattern recognition technology. For this target, a pattern recognition algorithm including user made software was developed and the total procedure including image processing and self-organizing map was treated by a backpropagation neural network, where image processing was composed of ultrasonic image acquisition, equalization filtering, binary processing and edge detection. Image processing and self-organizing map were compared as preprocessing methods for the reduction of dimensionality as input data into multi-layer perceptron or backpropagation neural networks. Also, the pattern recognition technique has been applied to classify two kinds of semiconductor defects: cracks and delamination. According to these results, it was found that the self-organizing map provided recognition rates of 83.4% and 75.7% for delamination and cracks, respectively, while BP provided 100% recognition rates for the results.

关键词： Neural networks Pattern recognition Image processing Artificial neural networks Delamination Application software Semiconductor devices Software algorithms backpropagation algorithms Filtering algorithms

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An RBF network method for blind signal separation

An RBF network method for blind signal separation

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ying Tan Jun Wang Department of Electronic Engineering and Information Science University of Science and Technology Hefei China Department of Automation and Computer Aided Engineering Chinese University of Hong Kong Hong Kong China

A radial basis function (RBF) based approach for blind signal separation in a nonlinear mixture is proposed. A cost function, which consists of the mutual information and partial moments of the outputs of the separation system, is defined to extract the independent signals from their nonlinear mixtures. The minimization of the cost function results in the independence of the outputs with desirable moments such that the original sources are separated properly. A learning algorithm for the parametric RBF network is established by using the stochastic gradient descent method. This approach is characterized by high learning convergence rate of weights, modular structure, as well as feasible hardware implementation. Simulation result demonstrates the feasibility, and validity of the proposed approach.

关键词： Radial basis function networks Blind source separation Signal processing algorithms Independent component analysis backpropagation algorithms Mutual information Data mining Brain modeling Vectors Information science

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An ART-based fuzzy adaptive learning control network

An ART-based fuzzy adaptive learning control network

引用

Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Cheng-Jian Lin Chin-Teng Lin Dept. of Control Eng. Nat. Chiao Tung Univ. Hsinchu Taiwan Department of Control Engineering National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)0780321251

This paper addresses the structure and associated on-line learning algorithms of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed Fuzzy Adaptive Learning COntrol Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure/parameter learning algorithm, called FALCON-ART, is proposed for constructing the FALCON dynamically. The FALCON-ART can partition the input/output space in a flexible way based on the distribution of the training data. Hence it can avoid the problem of combinatorial growing of partitioned grids in some complex systems. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. More notably the FALCON-ART can on-line partition the input/output spaces, tune membership functions and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these.< >

关键词： Fuzzy control Adaptive systems Programmable control Adaptive control Fuzzy logic Control systems Partitioning algorithms backpropagation algorithms Fuzzy systems Training data

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Neural metrics-software metrics in artificial neural networks

Neural metrics-software metrics in artificial neural network...

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International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES)

作者： W.K. Leung R. Simpson School of Computing Faculty of Computing Information and English University of Central England UK

backpropagation based supervised feedforward artificial neural networks (ANNs) have been developed for many applications (e.g. Rumelhart et al., 1986; Hinton, 1989; Werbos, 1990; and Riedmiller, 1994) but no detailed study of the measurement of the quality characteristics (e.g complexity and efficiency) of the network system has been made. Without an appropriate measurement, it is difficult to tell how the network performs on given applications. In addition, it is difficult to provide a measure of the algorithmic complexity of any given application. The paper proposes a new set of software metrics, named neural metrics, which provide indicative measures of the quality characteristics of ANNs. Neural metrics that are non-primitive in nature are defined mathematically as neural metrics functions.

关键词： Software metrics Intelligent networks Artificial neural networks backpropagation algorithms Neural networks Application software Training data Computer networks Logic testing Performance evaluation

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A study on neural networks for short-term load forecasting

A study on neural networks for short-term load forecasting

引用

International Forum on Applications of Neural Networks to Power Systems

作者： K.Y. Lee Y.T. Cha C.C. Ku Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

A study is made on the application of the artificial neural network (ANN) method to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern include Saturday, Sunday, and Monday loads. Three different ANN models are proposed, including two feedforward neural networks and one recurrent neural network. Inputs to the ANN are past loads and the output is the predicted load for a given day. The standard deviation and percent error of each model are compared.< >

关键词： Neural networks Load forecasting Artificial neural networks Load modeling Weather forecasting backpropagation algorithms Power system modeling Recurrent neural networks Predictive models Application software

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Hardware implementation of a real time neural network controller with a DSP and an FPGA

Hardware implementation of a real time neural network contro...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Sung Su Kim Seul Jung Department of Mechatronics Engineering Chungnam National University Daejeon South Korea

In this paper, we implement the intelligent controller hardware such as a neural network controller with an FPGA based general purpose controller and a DSP board to solve nonlinear control problems. The designed control hardware can perform a real time control of the backpropagation learning algorithm of a neural network. The basic PID control algorithms are implemented in an FPGA chip and a neural network controller is implemented in a DSP board. By using high capacity of an FPGA, the additional hardware such as an encoder counter and a PWM generator can be implemented in a single FPGA device. As a result, the controller is very cost effective. In order to show the performance of the controller, it was tested for controlling nonlinear systems such as an inverted pendulum.

关键词： Neural network hardware Neural networks Digital signal processing Field programmable gate arrays Digital signal processing chips backpropagation algorithms Pulse width modulation Nonlinear control systems Control systems Intelligent networks

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